December 4, 2019

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The identities of bots: A learning architecture for conversational software

The identities of bots: A learning architecture for conversational software

The recent explosion of bots on communication platforms has rekindled the hopes of conversational AI. However, building intelligent and customizable bots is not just bottlenecked by NLP and speech recognition. Our biggest limitation is the inability to modularize the goals of human bot interconnection. Suman Roy explains why we need a layered architecture for bots to learn about us from data.

Talk Title The identities of bots: A learning architecture for conversational software
Speakers Suman Roy (betaworks)
Conference O’Reilly Artificial Intelligence Conference
Conf Tag
Location New York, New York
Date September 26-27, 2016
URL Talk Page
Slides Talk Slides

Current AI bots fail to achieve anything drastically more advanced than reducing the number of taps we make on a phone. Implementing superior conversational agents depends on engineering several attributes: the ability to comprehend topics, recollecting past discussions in context, intelligently parsing digital feeds to save time, grasping sentiment and emotions in dialogue, and even exhibiting personality. The major computational bottleneck in achieving these capabilities appears to be the machine’s inability to restrict error propagation in a handful of NLP or speech recognition tasks. There’s an ancient saying: any problem in computer science can be solved by adding a layer of indirection. It is epitomized by the greatest invention of our times—the Internet, which is built on a layered architecture. When we chat with someone across the globe, the geographical network constraints necessitate a layered architecture for accurate message transmission. Similarly, to chat with conversational bots, cognitive constraints would require a layered architecture for accurate message comprehension. By modularizing the desired qualities in human bot interconnection, conversational agents can learn from data but keep the error propagation contained within specific modules. Since many conversational abilities have some codependence, modules with specific abilities can be stacked on top of each other in layers. Machine learning through a layered architecture can unleash the true potential of conversational bots. Suman Roy explores the architectural layers for human bot interconnection and the protocols that govern the learning paradigms. Each layer possesses capabilities that are built on top of abilities owned by lower layers, and the ability of each layer is exposed via protocols. Several machine-learning tools and algorithms already fit into specific layers in this architecture, and humans have their roles to play as well. As we move up the layers, human involvement intensifies because current computational methods aren’t accurate enough. On the other hand, algorithms in lower layers are more robust and time-tested in production environments.

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